Electrical and computer engineers want to mimic the brain’s visual system to create AI tools for guiding autonomous systems.
Electrical and computer engineers want to mimic the brain’s visual system to create AI tools for guiding autonomous systems.
The artificial intelligence systems that guide drones and self-driving cars rely on neural networks—trainable computing systems inspired by the human brain. But the digital computers they run on were initially designed for general-purpose computing tasks ranging from word processing to scientific calculations and have ultra-high reliability at the expense of high-power consumption.
To explore novel computer systems that are energy efficient particularly for machine learning, engineers at the University of Rochester are developing new analog hardware, with the possible application toward more efficient drones. Rochester engineers are attempting to do so by abandoning conventional state-of-the-art neural networks developed on digital hardware for computer vision. Instead, they’re turning to predictive coding networks, which are based on neuroscience theories that the brain has a mental model of the environment and constantly updates it based on feedback from the eyes.
Read more: University of Rochester
Photo Credit: Herney via Pixabay
