Large-scale Group Brainstorming using Conversational Swarm Intelligence (CSI) versus Traditional Chat

Conversational Swarm Intelligence (CSI) is an AI-facilitated method for enabling real-time conversational deliberations and prioritizations among networked human groups of potentially unlimited size. Based on the biological principle of Swarm Intelligence and modelled on the decision-making dynamics of fish schools, CSI has been shown in prior studies to amplify group intelligence, increase group participation, and facilitate productive collaboration among hundreds of participants at once. It works by dividing a large population into a set of small subgroups that are woven together by real-time AI agents called Conversational Surrogates. The present study focuses on the use of a CSI platform called Thinkscape to enable real-time brainstorming and prioritization among groups of 75 networked users. The study employed a variant of a common brainstorming intervention called an Alternative Use Task (AUT) and was designed to compare through subjective feedback, the experience of participants brainstorming using a CSI structure vs brainstorming in a single large chat room. This comparison revealed that participants significantly preferred brainstorming with the CSI structure and reported that it felt (i) more collaborative, (ii) more productive, and (iii) was better at surfacing quality answers. In addition, participants using the CSI structure reported (iv) feeling more ownership and more buy-in in the final answers the group converged on and (v) reported feeling more heard as compared to brainstorming in a traditional text chat environment. Overall, the results suggest that CSI is a very promising AI-facilitated method for brainstorming and prioritization among large-scale, networked human groups.

Towards Collective Superintelligence: Amplifying Group IQ using Conversational Swarms

Swarm Intelligence (SI) is a natural phenomenon that enables biological groups to amplify their combined intellect by forming real-time systems. Artificial Swarm Intelligence (or Swarm AI) is a technology that enables networked human groups to amplify their combined intelligence by forming similar systems. In the past, swarm-based methods were constrained to narrowly defined tasks like probabilistic forecasting and multiple-choice decision making. A new technology called Conversational Swarm Intelligence (CSI) was developed in 2023 that amplifies the decision-making accuracy of networked human groups through natural conversational deliberations. The current study evaluated the ability of real-time groups using a CSI platform to take a common IQ test known as Raven's Advanced Progressive Matrices (RAPM). First, a baseline group of participants took the Raven's IQ test by traditional survey. This group averaged 45.6% correct. Then, groups of approximately 35 individuals answered IQ test questions together using a CSI platform called Thinkscape. These groups averaged 80.5% correct. This places the CSI groups in the 97th percentile of IQ test-takers and corresponds to an effective IQ increase of 28 points (p<0.001). This is an encouraging result and suggests that CSI is a powerful method for enabling conversational collective intelligence in large, networked groups. In addition, because CSI is scalable across groups of potentially any size, this technology may provide a viable pathway to building a Collective Superintelligence.

Conversational Swarm Intelligence, a Pilot Study

Conversational Swarm Intelligence (CSI) is a new method for enabling large human groups to hold real-time networked conversations using a technique modeled on the dynamics of biological swarms. Through the novel use of conversational agents powered by Large Language Models (LLMs), the CSI structure simultaneously enables local dialog among small deliberative groups and global propagation of conversational content across a larger population. In this way, CSI combines the benefits of small-group deliberative reasoning and large-scale collective intelligence. In this pilot study, participants deliberating in conversational swarms (via text chat) (a) produced 30% more contributions (p<0.05) than participants deliberating in a standard centralized chat room and (b) demonstrated 7.2% less variance in contribution quantity. These results indicate that users contributed more content and participated more evenly when using the CSI structure.

The Manipulation Problem: Conversational AI as a Threat to Epistemic Agency

The technology of Conversational AI has made significant advancements over the last eighteen months. As a consequence, conversational agents are likely to be deployed in the near future that are designed to pursue targeted influence objectives. Sometimes referred to as the "AI Manipulation Problem," the emerging risk is that consumers will unwittingly engage in real-time dialog with predatory AI agents that can skillfully persuade them to buy particular products, believe particular pieces of misinformation, or fool them into revealing sensitive personal data. For many users, current systems like ChatGPT and LaMDA feel safe because they are primarily text-based, but the industry is already shifting towards real-time voice and photorealistic digital personas that look, move, and express like real people. This will enable the deployment of agenda-driven Virtual Spokespeople (VSPs) that will be highly persuasive through real-time adaptive influence. This paper explores the manipulative tactics that are likely to be deployed through conversational AI agents, the unique threats such agents pose to the epistemic agency of human users, and the emerging need for policymakers to protect against the most likely predatory practices.

Unique Identification of 50,000+ Virtual Reality Users from Head & Hand Motion Data

With the recent explosive growth of interest and investment in virtual reality (VR) and the so-called "metaverse," public attention has rightly shifted toward the unique security and privacy threats that these platforms may pose. While it has long been known that people reveal information about themselves via their motion, the extent to which this makes an individual globally identifiable within virtual reality has not yet been widely understood. In this study, we show that a large number of real VR users (N=55,541) can be uniquely and reliably identified across multiple sessions using just their head and hand motion relative to virtual objects. After training a classification model on 5 minutes of data per person, a user can be uniquely identified amongst the entire pool of 50,000+ with 94.33% accuracy from 100 seconds of motion, and with 73.20% accuracy from just 10 seconds of motion. This work is the first to truly demonstrate the extent to which biomechanics may serve as a unique identifier in VR, on par with widely used biometrics such as facial or fingerprint recognition.