The Inner Loop Of Collective Human Machine Intelligence

With the rise of artificial intelligence (AI) and the desire to ensure that such machines work well with humans, it is essential for AI systems to actively model their human teammates, a capability referred... In this paper, we introduce the inner loop of human-machine teaming expressed as communication with MToM capability. We present three different approaches to MToM: (1) constructing models of human inference with well-validated psychological theories and empirical measurements; (2) modeling human as a copy of the AI; and (3) incorporating well-documented domain... We offer a formal language for machine communication and MToM, where each term has a clear mechanistic interpretation. We exemplify the overarching formalism and the specific approaches in two concrete example scenarios. Related work that demonstrates these approaches is highlighted along the way.

The formalism, examples, and empirical support provide a holistic picture of the inner loop of human-machine teaming as a foundational building block of collective human-machine intelligence. Keywords: Artificial social intelligence; Cognitive science; Human computer interaction; Human–machine teaming. © 2023 The Authors. Topics in Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society. Conceptual overview. The inner loop of human–machine teaming hinges on the machine's communication…

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They’re expected to deliver today and drive… The most successful digital transformation strategies rely on constant coordination between people and technology. Artificial Intelligence (AI) is advancing at a remarkable clip, with breakthroughs, developments, and applications emerging so frequently that they can feel almost routine. Companies are rendering benchmarks obsolete quicker than we can devise more accurate ways to measure their capabilities or even define the goalposts. Less than a decade ago, AlphaGo’s victory over Lee Sedol was regarded as a “watershed moment” for AI. Today, achievements of arguably similar magnitude—such as Gemini 2.5 Deep Think earning a gold medal at the International Collegiate Programming Contest ​in September 2025​​​​​—are reported with increasing regularity.

However, AI’s significance does not lie in any single breakthrough, nor in speculative visions of artificial general intelligence (AGI) or “singularity” scenarios. Nor does it lie in the promised first-mover advantages that states believe they might earn if they can “dominate” AI first. Like electricity, the railroad, or the telegraph in earlier eras, AI’s real value lies in being an enabling, general-purpose technology—in this case, one that generates efficiencies, makes messy data usable, and builds better tools... The very promise of AI, however, creates a parallel challenge. The same systems that reduce cognitive load and augment human capacity also risk encouraging overreliance and inappropriate offloading of judgment to machines, a phenomenon known as automation bias. On the battlefield, the stakes of the promise and peril of AI are obvious.

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AI is the natural next step in the evolution of the precision-strike complex, which accelerated during the Gulf Wars, but whose ancestry dates back to experiments conducted in the 1930s and 1940s to improve... Over the course of decades of advances in guidance, sensors, and autonomy, those early ideas have been refined into weapons that can loiter, autonomously search for targets, and carry out single-use strikes—now with AI...

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