Combining Collective And Machine Intelligence At The Knowledge Frontie

Bonisiwe Shabane

-Nov 29, 2025, 8:54 PM

combining collective and machine intelligence at the knowledge frontie

Academia.edu no longer supports Internet Explorer. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser. 2023, Organisation for Economic Co-operation and Development eBooks Rarely a week passes without announcements that artificial intelligence (AI) has achieved new capabilities. Since the arrival of generative AI, ChatGPT and subsequent large language models-after many of the contributions to this book were written-discussion of AI's proliferating uses and their implications is increasingly visible in mainstream media. The economic, business, labour market and societal ramifications of AI now occupy the attention of firms, professional bodies, governmental and nongovernmental organisations.

Indeed, most governments in OECD countries have national AI strategies. Amid these developments, and except for specialised journals, less consideration has been given to the role of AI in research. This may be inevitable, as science is a specialised field. However, raising the productivity of research may be the most valuable of all the uses of AI. Being able to discover more scientific knowledge, helping science become more efficient, and doing this more quickly, will strengthen the foundations critical to addressing global challenges. Applying AI to research could be as transformative as the rise of systematised and institutionalised research and development in the postwar era.

Preparing for new contagions, generating technologies that elevate living standards, countering the diseases of ageing, producing clean energy, creating environmentally benign materials, and other overarching goals, all require technologies and innovations that emerge from... In this context, it gives us great pleasure to present this publication, Artificial Intelligence in Science: Challenges, Opportunities and the Future of Research. Gathering the views of leading practitioners and researchers, but written in non-technical language, this publication is addressed to a wide readership, including the public, policymakers, and stakeholders in all parts of science. Among other topics examined are: AI's current, emerging and possible future uses in science, including a number of rarely discussed applications; where progress in AI is needed to better serve science; changes in the... A distinctive contribution is the book's examination of policies for AI in science. Policymakers and actors across research systems can do much to maximise the society-wide benefits of AI in science, deepening AI's use in science, while also addressing the fast-changing implications of AI for research governance.

This publication is the fruit of a collaboration between our two organisations. The OECD's Directorate for Science, Technology and Innovation undertook the substantive work, under the aegis of its Committee for Scientific and Technological Policy. The publication and the wider project of which it is a part have been made possible thanks to financial and other support from the Fondation IPSEN (https://www.ipsen.com/ourcompany/ipsen-foundation/), which works to improve living conditions by... Pakistan Journal of Society, Education and Language (PJSEL, 2023 Correspondence should be sent to Pranav Gupta, Gies College of Business, University of Illinois, Urbana‐Champaign, 6 Wohlers Hall, 1206 S. Sixth St., Champaign, IL 61820, USA.

Email: pranavgu@illinois.edu Revised 2023 Jun 12; Received 2022 Jun 30; Accepted 2023 Jun 12; Issue date 2025 Apr. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa.

Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain—Collective Human‐Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence... We connect this with synergistic work on a compatible cognitive architecture, instance‐based learning theory and apply it to the design of AI agents that collaborate with humans.

We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human–machine... Nature Machine Intelligence volume 6, pages 251–264 (2024)Cite this article One vision of a future artificial intelligence (AI) is where many separate units can learn independently over a lifetime and share their knowledge with each other. The synergy between lifelong learning and sharing has the potential to create a society of AI systems, as each individual unit can contribute to and benefit from the collective knowledge. Essential to this vision are the abilities to learn multiple skills incrementally during a lifetime, to exchange knowledge among units via a common language, to use both local data and communication to learn, and... The result is a network of agents that can quickly respond to and learn new tasks, that collectively hold more knowledge than a single agent and that can extend current knowledge in more diverse...

Open research questions include when and what knowledge should be shared to maximize both the rate of learning and the long-term learning performance. Here we review recent machine learning advances converging towards creating a collective machine-learned intelligence. We propose that the convergence of such scientific and technological advances will lead to the emergence of new types of scalable, resilient and sustainable AI systems. This is a preview of subscription content, access via your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription

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Combining Collective And Machine Intelligence At The Knowledge Frontie

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Email: Pranavgu@illinois.edu Revised 2023 Jun 12; Received 2022 Jun 30;