In the natural world, Swarm Intelligence (SI) is a well-known phenomenon that enables groups of organisms to make collective decisions with significantly greater accuracy than the individuals could do on their own. In recent years, a new AI technology called Artificial Swarm Intelligence (ASI) has been developed that enables similar benefits for human teams. It works by connecting networked teams into real-time systems modeled on natural swarms. Referred to commonly as “human swarms” or “hive minds,” these closed-loop systems have been shown to amplify group performance across a wide range of tasks, from financial forecasting to strategic decision-making. The current study explores the ability of ASI technology to amplify the IQ of small teams. Five small teams answered a series of questions from a commonly used intelligence test known as the Raven’s Standard Progressive Matrices (RSPM) test. Participants took the test first as individuals, and then as groups moderated by swarming algorithms (i.e. “swarms”). The average individual achieved 53.7% correct, while the average swarm achieved 76.7% correct, corresponding to an estimated IQ increase of 14 points. When the individual responses were aggregated by majority vote, the groups scored 56.7% correct, still 12 IQ points less than the real-time swarming method.
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Artificial Swarm Intelligence employed to Amplify Diagnostic Accuracy in Radiology
Swarm Intelligence (SI) is a biological phenomenon in which groups of organisms amplify their combined intelligence by forming real-time systems. It has been studied for decades in fish schools, bird flocks, and bee swarms. Recent advances in networking and AI technologies have enabled distributed human groups to form closed-loop systems modeled after natural swarms. The process is referred to as Artificial Swarm Intelligence (ASI) and has been shown to significantly amplify group intelligence. The present research applies ASI technology to the field of medicine, exploring if small groups of networked radiologists can improve their diagnostic accuracy when reviewing chest X-rays for the presence of pneumonia by “thinking together” as an ASI system. Data was collected for individual diagnoses as well as for diagnoses made by the group working as a real-time ASI system. Diagnoses were also collected using a state-of-the-art deep learning system developed by Stanford University School of Medicine. Results showed that a small group of networked radiologists, when working as a real-time closed-loop ASI system, was significantly more accurate than the individuals on their own, reducing errors by 33%, as well as significantly more accurate (22%) than a state- of-the-art software-only solution using deep learning.
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- Award ID(s):
- 1840937
- PAR ID:
- 10125861
- Date Published:
- Journal Name:
- IEMCON 2018
- Page Range / eLocation ID:
- 1186 to 1191
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Many social species amplify their decision-making accuracy by deliberating in real-time closed-loop systems. Known as Swarm Intelligence (SI), this natural process has been studied extensively in schools of fish, flocks of birds, and swarms of bees. The present research looks at human groups and tests their ability to make financial forecasts by working together in systems modeled after natural swarms. Specifically, groups of financial traders were tasked with forecasting the weekly trends of four common market indices (SPX, GLD, GDX, and Crude Oil) over a period of 19 consecutive weeks. Results showed that individual forecasters, who averaged 56.6% accuracy when predicting weekly trends on their own, amplified their accuracy to 77.0% when predicting together as real-time swarms. This reflects a 36% increase in forecasting accuracy and shows high statistical significance (p<0.001). Further, if investments had been made according to these swarm-based forecasts, the group would have netted a 13.3% return on investment (ROI) over the 19 weeks, compared to the individual’s 0.7% ROI. This suggests that enabling groups of traders to form real-time systems online, governed by swarm intelligence algorithms, has the potential to significantly increase the accuracy and ROI of financial forecasts.more » « less
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The local force field generated by light endows optical microrobots with remarkable flexibility and adaptivity, promising significant advancements in precise medicine and cell transport. Nevertheless, the automated navigation of multiple optical microrobots in intricate, dynamic environments over extended distances remains a challenge. Herein, a versatile control strategy aimed at navigating optical microrobotic swarms to distant targets under obstacles of varying sizes, shapes, and velocities is introduced. By confining all microrobots within a manipulation domain, swarm integrity is ensured while mitigating the effects of Brownian motion. Obstacle's elliptical approximation is developed to facilitate efficient obstacle avoidance for microrobotic swarms. Additionally, several supplementary functions are integrated to enhance swarm robustness and intelligence, addressing uncertainties such as swarm collapse, particle immobilization, and anomalous laser–obstacle interactions in real microscopic environments. We further demonstrate the efficacy and versatility of our proposed strategy by achieving autonomous long‐distance navigation to a series of targets. This strategy is compatible with both optical trapping‐ and nudging‐based microrobotic swarms, representing a significant advancement in enabling optical microrobots to undertake complex tasks such as drug delivery and nanosurgery and understanding collective motions.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IEMCON.2018.8614883
