Artificial Swarm Intelligence vs human experts
Artificial Swarm Intelligence vs human experts
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人工智能群体与人类专家
DOI：
10.1109/ijcnn.2016.7727517
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Louis B. Rosenberg
中科院分区：
文献类型：
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作者：
Louis B. Rosenberg
人工群智能（ASI）致力于通过在模仿生物群的闭环系统中连接人类用户群来促进超人智慧的出现。此前的研究表明，与传统的利用群体智慧的方法（如投票和民意调查）相比，“人类群体”可以做出更准确的预测。为了进一步测试群集的预测能力，75名随机的体育迷聚集在联合国大学的人类群集平台上，并负责预测大学碗足球比赛的蔓延。ESPN的专家预测进行了比较。结果如下：（i）个人-当单独工作时，测试对象平均在10场比赛中实现5次正确预测（50%的准确率）;（ii）群体调查-汇总所有75名受试者的数据，该群体在10场比赛中实现了6次正确预测（60%的准确率）;（iii）专家-根据ESPN公布的数据，大学足球专家在10场比赛中平均预测正确5次（50%的准确率）;（iv）群-当75名受试者作为实时群一起工作时，他们在10场比赛中获得了7个正确的预测（70%的准确率）。因此，通过形成实时的群体智能，随机的体育迷群体提高了他们的集体表现，并超过了专家。
Artificial Swarm Intelligence (ASI) strives to facilitate the emergence of a super-human intellect by connecting groups of human users in closed-loop systems modeled after biological swarms. Prior studies have shown that “human swarms” can make more accurate predictions than traditional methods for tapping the wisdom of groups, such as votes and polls. To further test the predictive ability of swarms, 75 random sports fans were assembled in the UNU platform for human swarming and tasked with predicting College Bowl football games against the spread. Expert predictions from ESPN were compared. The results are as follows: (i) Individuals - when working alone, test subjects achieved on average, 5 correct predictions out of 10 games (50% accuracy); (ii) Group Poll - aggregating data across all 75 subjects, the group achieved 6 correct predictions out of 10 games (60% accuracy); (iii) Experts - as published by ESPN, the college football experts averaged 5 correct predictions out of 10 games (50% accuracy); and (iv) Swarm - when the 75 subjects worked together as a real-time swarm, they achieved 7 correct predictions out of 10 games (70% accuracy). Thus by forming a real-time swarm intelligence, the group of random sports fans boosted their collective performance and out-performed experts.