To better understand how neural networks learn to simulate writing, researchers trained simpler versions on synthetic children’s stories.

In order for robots to effectively partake in search and rescue operations, they need to effectively navigate obstacles in their way. One area that is particularly common and difficult to venture into is vegetation.

Robots typically use a combination of sensors to perceive their surroundings such as ultrasonic sensors, Lidar (Light Detection and Ranging), infrared sensors and camera systems. However, these are not often enough to allow robots to actually bypass the vegetation so commonly found in real outdoor environments.

That’s why engineers at Carnegie Mellon University are working on solving this particular dilemma.

Instead of looking at individual neurons, they look at combinations of neurons that form patterns or features.

Artificial neural networks are like digital versions of our brains. They learn from data, not rules, and they can perform extraordinary tasks, from translating languages to playing chess. But how do they do it? What is the logic behind their calculations? And how can we trust them to be safe and reliable?

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AI brains: How do they work?