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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="Using graph neural networks (GNNs) allows points to “communicate” and self-optimize for better uniformity. Their approach helps optimize point placement to handle complex, multi-dimensional problems necessary for accurate simulations (Image: Alex Shipps/MIT CSAIL)."
CSAIL article

Imagine you’re tasked with sending a team of football players onto a field to assess the condition of the grass (a likely task for them, of course). If you pick their positions randomly, they might cluster together in some areas while completely neglecting others. But if you give them a strategy, like spreading out uniformly across the field, you might get a far more accurate picture of the grass condition.

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A system developed by MIT CSAIL researchers can oversee a team of both human and AI agents, communicating with team members to align roles and accomplish a common goal (Credits: Alex Shipps/MIT CSAIL).
CSAIL article

On a research cruise around Hawaii in 2018, Yuening Zhang SM ’19, PhD ’24 saw how difficult it was to keep a tight ship. The careful coordination required to map underwater terrain could sometimes led to a stressful environment for team members, who might have different understandings of which tasks must be completed in spontaneously changing conditions. During these trips, Zhang considered how a robotic companion could have helped her and her crewmates achieve their goals more efficiently.

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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.