AI Neural Networks typically aim to replicate the functions of the human brain. But with some 86 billion neurons in our brains, well, that makes for a pretty complicated machine. Lots of potential, sure, and fascinating details to explore as they develop, but it's still a lot to handle.
Writing in IEEE Spectrum, Frances Chance proposes a simpler but more efficient solution: modeling AI off of dragonfly brains.
In my research at Sandia National Laboratories in Albuquerque, I study the brains of one of these larger insects, the dragonfly. I and my colleagues at Sandia, a national-security laboratory, hope to take advantage of these insects' specializations to design computing systems optimized for tasks like intercepting an incoming missile or following an odor plume. By harnessing the speed, simplicity, and efficiency of the dragonfly nervous system, we aim to design computers that perform these functions faster and at a fraction of the power that conventional systems consume.
If you have ever encountered a dragonfly, you already know how fast these beautiful creatures can zoom, and you've seen their incredible agility in the air. Maybe less obvious from casual observation is their excellent hunting ability: Dragonflies successfully capture up to 95 percent of the prey they pursue, eating hundreds of mosquitoes in a day.
The physical prowess of the dragonfly has certainly not gone unnoticed. For decades, U.S. agencies have experimented with using dragonfly-inspired designs for surveillance drones. Now it is time to turn our attention to the brain that controls this tiny hunting machine.
If nothing else, I feel like this should alleviate some concerns about AI gaining sentience and turning against mankind. A dragonfly ain't gonna go all Skynet on us, right?
Fast, Efficient Neural Networks Copy Dragonfly Brains [Frances Chance / IEEE Spectrum]
Image via Public Domain Pictures