Perhaps the most remarkable research is that of Cornell bee scientist Thomas Seeley, who has demonstrated that honeybee language extends beyond foraging behavior. For several decades, Seeley focused his research on bee swarming. Swarming is the way honeybee colonies naturally reproduce; a single colony splits into two or more distinct colonies, and one group flies off to find a new home. How, Seeley wondered, did the colony decide on their preferred site?
When Seeley first decided to focus on swarming, scientists knew very little about the phenomenon. The fastest bees in a swarm fly over 20 miles per hour, usually moving in a straight line toward their target regardless of the fields, water bodies, buildings, hills or forests in their way. There is no way a human can keep up with the swarm, much less keep track of several thousand individual bees to figure out which ones, if any, are guiding the rest. Seeley was interested in how the bees decided which home to select — a high-stakes decision, given that splitting the hive could cause the queen to be lost, and choosing an inappropriate site could lead to the death of the hive.
In the mid-2000s, Seeley convinced a computer engineer who was intrigued by the similarities between bee swarms and driverless cars to install a high-powered video camera at Seeley’s research site on Appledore Island, off the coast of Maine. Their goal was to create an algorithm that could automatically identify and track some 10,000 speeding bees at once.
After two painstaking years, the algorithm finally worked: Powered by high-speed digital cameras and novel techniques in computer vision, it could identify each individual bee from the video footage and analyze its unique frenzied flight pattern. The algorithm revealed patterns undetectable to the human eye; decoding the diversity, density and interactions in these patterns led Seeley to label the swarm as a “cognitive entity.”
Perhaps Seeley’s most startling finding was that, in choosing a new home, honeybees exhibit sophisticated forms of democratic decision-making, including collective fact-finding, vigorous debate, consensus building, quorum and a complex stop signal enabling cross-inhibition, which prevents an impasse being reached. A bee swarm, in other words, is a remarkably effective democratic decision-making body in motion, which bears resemblance to some processes in the human brain and human society. Seeley went so far as to claim that the collective interactions of individual bees were strikingly similar to the interactions between our individual neurons when collectively arriving at a decision.
Seeley’s findings bolstered the arguments of those who argued in favor of referring to honeybee communication as language. And by demonstrating that the “hive mind” was more than mere metaphor, Seeley also stimulated advances in swarm intelligence in robotics and engineering. Seeley’s research, predicated on digital technology (computer vision and machine learning) eventually came full circle: His findings inspired computer scientists at Georgia Tech to create the Honey Bee algorithm, which is now an integral part of cloud computing: In internet hosting centers (analogous to hives), it optimizes the allocation of servers (foraging bees) among jobs (nectar sources), thereby helping to deal with sudden spikes in demand and preventing long queues. In 2016, Seeley and his collaborators were awarded the Golden Goose Award, which recognizes apparently esoteric research that later proves to be extremely valuable.
