我們總是說人類是萬物之靈，認為人類的智慧遠遠在其他的生物之上。然而隨著科技和研究方法的進步，我們現在更能理解和運用其他生物生存的智慧。
對於蜜蜂群的研究，就是一個很好的例子。
我們已經知道，蜜蜂可以循著味道以及花的顏色，來找到花粉和花蜜。而它們集體蜂湧(swarm)的行為，在高速攝影機及電腦的計算能力之下，我們有了進一步的了解：
… Seeley adapted digital technology to extend his experiments in a new direction: he 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: to create an algorithm that could automatically identify and track ten thousand 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. The 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採用數位科技把他的研究擴展到一個新的領域：他說服了一位電腦工程師(他對一群蜜蜂一起蜂湧(swarm)和一堆無人駕駛車輛的狀況的相似性十分著迷)，在Seekey 緬因州外海的Appledore島的研究地點裝了一駕高速攝影機。他們的目標是：創造一個可以同時自動偵測並追蹤一萬隻蜜蜂的演算法。經過兩年艱苦的努力，演算法終於完成：在高速數位相機和最新影像處理技術的加持之下，該演算法在影片片段中可以辨識出每一隻蜜蜂，並且分析它獨特狂亂的飛行模式。該演算法揭露了人眼無法看出來的模式，隨著對飛行模式中的多樣性、密度和互動頻率的解碼，Seeley把蜂湧(swarm)解讀為「一個有認知能力的整體」(cognitive entity)。 或許Seeley最令人驚嘆的發現是，在尋找一個地方建立新的蜂窩，蜜蜂展現了成熟的民主決策模式，包括集體的現況蒐集、激烈的爭論、累積共識、達到足夠的贊成多數、發出停止的訊號以避免陷入僵局等等。蜜蜂的蜂湧(swarm)，換句話說，是一個很了不起的有效民主決策體在運作，很像人的腦袋和人類社會中的決策程序。Seeley甚至進一步宣稱，個別蜜蜂之間的集體互動，非常類似當我們要做成一個決定，腦中個別神經元之間的關係。
Seeley’s findings, published in Science and widely popularized in the media, 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 two computer scientists at Georgia Tech to create the Honey Bee algorithm, which is now an integral part of the multibillion dollar cloud computer industry. The algorithm, widely used in internet hosting centers (analogous to hives), optimizes the allocation of servers (analogous to foraging bees) among jobs (analogous to nectar sources), thereby helping to deal with sudden spikes in demand and preventing long queues….
Seeley 的研究發現，刊登在Science雜誌並受到媒體的大量關注，加強了傾向於把蜜蜂間的溝通視為語言的研究者的論述。而且展現蜜蜂「心智的蜂巢」已經不只是一種比喻，Seeley也刺激了蜂踴(swarm)的智慧在機器人和工程界的進步。Seeley的研究，確定在數位科技(電腦影像處理和機器學習)終於完全成形：他的研究促進了Georgia Tech的兩位科學家創造了一個「蜜蜂」演算法，現在已經成為數十億美元價值的雲端計算產業的核心。該演算法，廣泛運用於互聯網主機中心(類比為蜂巢)，最適化伺服器的配置(類比為覓食的蜜蜂)和作業排程(類比為蜜源)。該演算法有助於應付突發的大量需求，預防長時間排隊等待…..
*： Karen Baker, 《Sounds of Life》，2022，Princeton University Press
2024/6/25 蜂踴(Swarm)的智慧 Damakey
