Collective Intelligence Research

How large language models can reshape collective intelligence

Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals—even experts—resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information technology, such as online prediction markets that elicit the ‘wisdom of crowds’, online forums that structure collective deliberation or digital platforms that crowdsource knowledge from the public. Large language models, however, are transforming how information is aggregated, accessed and transmitted online. Here we focus on the unique opportunities and challenges this transformation poses for collective intelligence. We bring together interdisciplinary perspectives from industry and academia to identify potential benefits, risks, policy-relevant considerations and open research questions, culminating in a call for a closer examination of how large language models affect humans’ ability to collectively tackle complex problems.

Sharing Notes about Collective Intelligence — Pop Junctions

Last week, my travels took me to San Antonio where I delivered one of the keynote addresses at the Educause Learning Initiative conference -- a gathering focused on the application of technology for learning at the college and university level. My presentation, "What Wikipedia Can Teach Us Ab

Socially Minded Intelligence: How Individuals, Groups, and Artificial Intelligence Can Make Each Other Smarter (or Not)

A core part of human intelligence is the ability to work flexibly with others to achieve goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically construe this either as an individual-difference trait or as a property of groups. We argue that by focusing either on individual or collective intelligence without considering their dynamic interaction, existing conceptualizations of intelligence limit the potential of people and AI systems. To address this impasse, we propose a new kind of intelligence, 'socially minded intelligence', that can be applied to both individuals and collectives. We outline how socially minded intelligence might be measured and cultivated within people, how it might be modelled in AI agents, and how it might be applied to other intelligent systems.

Profile: Adrien Treuille

The combined efforts of half a million video gamers could some day help cure a disease.

NOVA | The Wisdom of the Crowds | Season 45 | Episode 6

Crowds hold a predictive power that can have startling accuracy.

Mapping Citizen Science through the Lens of Human-Centered AI

Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss and categorize the potential of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of human-centered AI including HI, while also benefiting CS. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This “HI lens” provides the CS community with an overview of ways to utilize the combination of AI and human intelligence in their projects. For AI researchers, this work highlights the opportunity CS presents to engage with real-world data sets and explore new AI methods and applications.

Peer Production in Citizen Science: A Community-Centered Approach on the Example of Personal Science

Citizen science encompasses a wide range of practices where online collaboration for knowledge production plays a significant role. However, the study of forms of online collaboration other than crowdsourcing in citizen science has remained largely unexplored. This thesis aims to fill this gap by investigating peer production as a form of collaboration in online citizen science communities of practice. First, peer production theory was operationalized as a working model and used to analyze collaboration in citizen science case studies. This was followed by a comprehensive participatory design process for a specific use case involving the personal science community of practice. This process resulted in the creation of the “Personal Science Wiki”, an online space for consolidating community knowledge through peer production. Subsequently, a usability and card sorting study identified and resolved issues with the wiki implementation, and provided insights into mental models and content requirements regarding self-research knowledge. The lessons learned from the participatory design process were generalized as process recommendations for designing peer production solutions and knowledge management systems with communities of practice.

Human-Centered Artificial Intelligence: Three Fresh Ideas

Human-Centered AI (HCAI) is a promising direction for designing AI systems that support human self-efficacy, promote creativity, clarify responsibility, and facilitate social participation. These human aspirations also encourage consideration of privacy, security, environmental protection, social justice, and human rights. This commentary reverses the current emphasis on algorithms and AI methods, by putting humans at the center of systems design thinking, in effect, a second Copernican Revolution. It offers three ideas: (1) a two-dimensional HCAI framework, which shows how it is possible to have both high levels of human control AND high levels of automation, (2) a shift from emulating humans to empowering people with a plea to shift language, imagery, and metaphors away from portrayals of intelligent autonomous teammates towards descriptions of powerful tool-like appliances and tele-operated devices, and (3) a three-level governance structure that describes how software engineering teams can develop more reliable systems, how managers can emphasize a safety culture across an organization, and how industry-wide certification can promote trustworthy HCAI systems. These ideas will be challenged by some, refined by others, extended to accommodate new technologies, and validated with quantitative and qualitative research. They offer a reframe -- a chance to restart design discussions for products and services -- which could bring greater benefits to individuals, families, communities, businesses, and society.

Hybrid Intelligence

Business & Information Systems Engineering -

Beyond the Individual: Understanding the Evolution of Collective Intelligence

This chapter outlines the evolution of collective intelligence, starting from its ancient roots and concluding with modern digital platforms. It discusses intelligence theories, project examples, and the impact of technology on collaborative efforts. Key focuses include the role of the internet and online communities in boosting our collective IQ, with a particular emphasis on Douglas Engelbart's contributions and the open-source movement, as exemplified by Linux's development. The chapter examines how digital transformation has facilitated new forms of community and knowledge sharing, significantly influencing fields such as management, decision-making, and organizational learning. Various scholars and their definitions of CI are discussed, including Pierre Lévy's vision of universally distributed intelligence and the concept of swarm intelligence in biological sciences. We then move on to practically implemented CI projects, exploring crowdsourcing as a manifestation of CI in business and social projects and examining possibilities of harnessing the wisdom of crowds for problem-solving and innovation. The chapter concludes with a presentation of the current state of collective intelligence academic research.

Here Comes Everybody (book)

Here Comes Everybody: The Power of Organizing Without Organizations is a book by Clay Shirky published by Penguin Press in 2008 on the effect of the Internet on modern group dynamics and organization. The author considers examples such as Wikipedia, MySpace, and other social media in his analysis. According to Shirky, the book is about "what happens when people are given the tools to do things together, without needing traditional organizational structures". The title of the work alludes to HCE, a recurring and central figure in James Joyce's Finnegans Wake and considers the impacts of self-organizing movements on culture, politics, and business.

Super chickens, givers, and collective intelligence: the importance of collaboration, teamwork, and mentorship in science

Official websites use .gov A .gov website belongs to an official government organization in the United States.

(PDF) Why is dialogical solving of a logical problem more effective than individual solving?: A formal and experimental study of an abstract version of Wason’s task

PDF | We study the accomplishment of the abstract version of Wason’s selection task in a cooperative dialogue context that has been neglected in the... | Find, read and cite all the research you need on ResearchGate

Society-in-the-loop: programming the algorithmic social contract

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, ‘SITL = HITL + Social Contract.’

Emergence: the connected lives of ants, brains, cities and software

In the tradition of Being Digital and The Tipping Point…

Positive Sum Worlds: Remaking Public Goods

An era of global protocols requires a visionary redefinition of public goods, in service of others.

Squad Wealth

The squad is the basic user class for the tools we need today as a society.

Self-Organization in Biological Systems

The synchronized flashing of fireflies at night. The spiraling patterns of an aggregating slime mold. The anastomosing network of army-ant trails. The coordinated movements of a school of fish. Researchers are finding in such patterns—phenomena that have fascinated naturalists for centuries—a fertile new approach to understanding biological systems: the study of self-organization. This book, a primer on self-organization in biological systems for students and other enthusiasts, introduces readers to the basic concepts and tools for studying self-organization and then examines numerous examples of self-organization in the natural world. Self-organization refers to diverse pattern formation processes in the physical and biological world, from sand grains assembling into rippled dunes to cells combining to create highly structured tissues to individual insects working to create sophisticated societies. What these diverse systems hold in common is the proximate means by which they acquire order and structure. In self-organizing systems, pattern at the global level emerges solely from interactions among lower-level components. Remarkably, even very complex structures result from the iteration of surprisingly simple behaviors performed by individuals relying on only local information. This striking conclusion suggests important lines of inquiry: To what degree is environmental rather than individual complexity responsible for group complexity? To what extent have widely differing organisms adopted similar, convergent strategies of pattern formation? How, specifically, has natural selection determined the rules governing interactions within biological systems? Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology—a field of study at the forefront of life sciences research.

Why Information Grows

"Hidalgo has made a bold attempt to synthesize a large body of cutting-edge work into a readable, slender volume. This is the future of growth theory." -- F...

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