University of Kwazulu-Natal

Human Ai Teaming In The Age Of Collaborative Intelligence

Bonisiwe Shabane
-
human ai teaming in the age of collaborative intelligence

This article explores the evolving landscape of human-AI teaming, focusing on its transformative impact, adaptive intelligence in mixed-reality environments, collective intelligence, transparency challenges, and the transition toward collaboration. Introduction to human-AI teaming (understanding the shift, key concepts, and examples of collaborative intelligence) Expanding the definition of collaboration (moving beyond traditional AI roles, emphasizing real-time adaptability and dynamic role changes) Adaptive intelligence in mixed-reality environments (reinforcement learning, emergency response use cases, and user experience improvements) Emerging models of collaborative intelligence (DAOs, Web 3.0, and decentralized finance as examples of new forms of collective decision-making, shared governance, and resource allocation) Jacob Taylor, Thomas Kehler, Sandy Pentland, Martin Reeves

Janice C. Eberly, Molly Kinder, Dimitris Papanikolaou, Lawrence D. W. Schmidt, Jón Steinsson Rosanne Haggerty, Ruby Bolaria Shifrin, Jacob Taylor, Kershlin Krishna, Sara Bronin, Nick Cain, Xiomara Cisneros, Adam Ruege, Henri Hammond-Paul, Jamie Rife, Josh Humphries, Beth Noveck This article explores the evolving landscape of human-AI teaming, focusing on its transformative impact, adaptive intelligence in mixed-reality environments, collective intelligence, transparency challenges, and the transition toward collaboration.

Today, someone asked whether the rough draft above was written by me, Junior Williams , or by OpenAI 's ChatGPT 4o with canvas. I can definitively state it was written by me, utilizing my brain, various notepads, mind-maps, voice notes, text files, rich text files, other language models, and LinkedIn 's editing tools for formatting as well. That being said, questions around authenticity are essential — by questioning the authorship of this draft, we're engaging with the very themes of partnership and synergy that are central to the future of collaborative... It's not about determining whether a human or a machine is the creator, but about appreciating how the fusion of both can lead to outcomes neither could achieve alone. This issue examines the collaboration between humans and AI, focusing on human-in-the-loop models—a cornerstone of collaborative intelligence where human judgment and machine precision converge in real-time. These models facilitate dynamic and adaptive cooperation.

They expand the definition of collaborative intelligence to include not only human-machine teaming but also machine-machine and human-human collaboration. Innovation often arises at the intersection of these diverse forms of intelligence. Human-AI teaming redefines problem-solving by blending computational power with human intuition and creativity. It transcends task delegation, creating synergies where both entities actively enhance each other’s strengths. With advancements in AI technologies like large language models (LLMs), mixed-reality systems, and multi-modal generative frameworks, the boundary between human and machine capabilities is increasingly blurred. This partnership is unlocking new possibilities across fields such as healthcare, engineering, cybersecurity, and beyond.

In this issue, we examine diverse strategies for human-AI collaboration to tackle challenges and enhance creativity and productivity. arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

As I sat down with Jim Wilson, global managing director of thought leadership and technology at Accenture and co-author of the newly updated book Human + Machine: Reimagining Work in the Age of AI,... In a world where groundbreaking AI advancements seem to be delivered each month, Wilson offers a refreshingly optimistic perspective that cuts through the noise. Rather than viewing AI as a job-stealing threat, he presents compelling evidence for a future built on collaborative intelligence. "There's an emerging kind of collaborative intelligence that companies are going to need now to compete and innovate," Wilson explained during our conversation. "It's really about thoughtfully and rigorously creating that combined effect where human ingenuity, human innovation, plus AI systems outperform what either one could do alone." To illustrate this point, Wilson shared the fascinating story of a Lithuanian researcher who ingeniously repurposed AlphaFold (an AI system for predicting protein structures) to solve complex protein interaction problems that its creators hadn't...

The result? A scientific breakthrough that combined human creativity with AI processing power. "On the human side, previous methods could achieve about 74 percent accuracy. But that often took weeks of manual effort," Wilson noted. "On the AI side, AlphaFold would have essentially scored a zero. But through human and machine collaboration, we actually see an effect where they were able to achieve 88 percent precision in just a few hours."

Your research is the real superpower - learn how we maximise its impact through our leading community journals Work in the future will be a partnership between people, agents, and robots—all powered by artificial intelligence. While much of the current public debate revolves around whether AI will lead to sweeping job losses, our focus is on how it will change the very building blocks of work—the skills that underpin... Our research suggests that although people may be shifted out of some work activities, many of their skills will remain essential. They will also be central in guiding and collaborating with AI, a change that is already redefining many roles across the economy. In this research, we use “agents” and “robots” as broad, practical terms to describe all machines that can automate nonphysical and physical work, respectively.

Many different technologies perform these functions, some based on AI and others not, with the boundaries between them fluid and changing. Using the terms in this expansive way lets us analyze how automation reshapes work overall.1Our analysis considers a broader range of automation technologies than the narrow definition of agents commonly used in the AI... For more on how we define the term, see the Glossary. This report builds on McKinsey’s long-running research on automation and the future of work. Earlier studies examined individual activities, while this analysis also looks at how AI will transform entire workflows and what this means for skills. New forms of collaboration are emerging, creating skill partnerships between people and AI that raise demand for complementary human capabilities.

Although the analysis focuses on the United States, many of the patterns it reveals—and their implications for employers, workers, and leaders—apply broadly to other advanced economies. We find that currently demonstrated technologies could, in theory, automate activities accounting for about 57 percent of US work hours today.2Our analysis focuses exclusively on paid productive hours in the US workforce, encompassing full-time... We assess only the share of time awake that is spent on work-related activities, totaling roughly 45 percent of waking hours. Our analysis excludes time spent on unpaid tasks and leisure, but agents and robots could be used in related activities to support productivity and personal well-being. This estimate reflects the technical potential for change in what people do, not a forecast of job losses. As these technologies take on more complex sequences of tasks, people will remain vital to make them work effectively and do what machines cannot.

Our assessment reflects today’s capabilities, which will continue to evolve, and adoption may take decades.

People Also Search

This Article Explores The Evolving Landscape Of Human-AI Teaming, Focusing

This article explores the evolving landscape of human-AI teaming, focusing on its transformative impact, adaptive intelligence in mixed-reality environments, collective intelligence, transparency challenges, and the transition toward collaboration. Introduction to human-AI teaming (understanding the shift, key concepts, and examples of collaborative intelligence) Expanding the definition of collab...

Janice C. Eberly, Molly Kinder, Dimitris Papanikolaou, Lawrence D. W.

Janice C. Eberly, Molly Kinder, Dimitris Papanikolaou, Lawrence D. W. Schmidt, Jón Steinsson Rosanne Haggerty, Ruby Bolaria Shifrin, Jacob Taylor, Kershlin Krishna, Sara Bronin, Nick Cain, Xiomara Cisneros, Adam Ruege, Henri Hammond-Paul, Jamie Rife, Josh Humphries, Beth Noveck This article explores the evolving landscape of human-AI teaming, focusing on its transformative impact, adaptive intelli...

Today, Someone Asked Whether The Rough Draft Above Was Written

Today, someone asked whether the rough draft above was written by me, Junior Williams , or by OpenAI 's ChatGPT 4o with canvas. I can definitively state it was written by me, utilizing my brain, various notepads, mind-maps, voice notes, text files, rich text files, other language models, and LinkedIn 's editing tools for formatting as well. That being said, questions around authenticity are essent...

They Expand The Definition Of Collaborative Intelligence To Include Not

They expand the definition of collaborative intelligence to include not only human-machine teaming but also machine-machine and human-human collaboration. Innovation often arises at the intersection of these diverse forms of intelligence. Human-AI teaming redefines problem-solving by blending computational power with human intuition and creativity. It transcends task delegation, creating synergies...

In This Issue, We Examine Diverse Strategies For Human-AI Collaboration

In this issue, we examine diverse strategies for human-AI collaboration to tackle challenges and enhance creativity and productivity. arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data pr...