Why autonomous systems should mirror the structure of biological intelligence

We stand at the threshold of an era defined not by incremental upgrades but by system-level reimagination: moving from connected to autonomous systems.

Whether it be self-driving cars, smart factories, digital health platforms or intelligent cities – artificial intelligence (AI) drives the promise of autonomy.

However, true autonomy emerges from the intentional convergence of sensing, connectivity, computing and control – not isolated intelligence.

Layered, secure and context-aware, this convergence mirrors the time-tested structure of biological intelligence, which is the system used by living organisms to process information.

Indeed, biological intelligence must serve as the foundational design principle for building next-generation autonomous systems. To do this, we need to bring together national and international experts across related research areas.

The human body is the most advanced autonomous system we know, seamlessly integrating perception, cognition, communication and action. If we are to engineer autonomy at scale, we must build systems that replicate this harmony.

Like humans rely on their senses, autonomous systems depend on sensors. Cameras, LiDAR, acoustic sensors, biosensors and tactile interfaces provide a continuous stream of environmental data. High-fidelity, multimodal sensing defines the richness and resolution of a system's perception and, ultimately, the quality of its decisions.

Connectivity links the components of the system just as the human nervous system transmits sensory information. The autonomous system requires secure, low-latency communication pathways – from 5G/6G wireless networks and time-sensitive networking to optical and quantum links. This layer ensures the seamless movement of information, enabling distributed intelligence and coordinated action.

The computing layer functions as the system's brain. Here, data transforms into insight and insight into intent. Distributed across the edge, cloud and embedded systems, computing infrastructure leverages AI and machine learning, neuromorphic processors – computer chips that mimic the human brain – and agentic frameworks. The goal is not just to automate responses but to enable reasoning, learning and real-time adaptation.

Finally, through robotics, machine-to-machine (M2M) and machine-to-environment (M2E) interfaces, autonomous agents execute commands, manipulate environments or communicate outcomes. These actuators form the physical extensions of the system's cognition, closing the loop between perception and impact.

Autonomy is the outcome of system-level innovation that harmonizes disciplines. It must incorporate a design that bridges wireless, AI, edge computing, control theory and hardware. The orchestration between them is vital to manage distributed real-time constraints.

It should also be secure by design, with zero-trust communication and policy-based access. We have to ensure that the system is context-aware and aligned with the end-user's goals and environmental constraints.

Systemic thinking transforms autonomy from a product feature to a foundational capability. Smart systems are already realising this architecture:

In each case, autonomy is not a bolt-on feature, but an emergent property of systems designed for convergence.

The future of autonomy will be shaped by technologies that blur the boundaries between sensing, communication, computing and control.

In this world, communication will become sensing. Integrated sensing and communication (ISAC) will turn wireless networks into perceptual platforms. Radio signals will detect motion, measure distance and map environments. The network itself will become a distributed sensor, enabling ambient awareness without dedicated hardware.

The advent of 6G will introduce a network compute fabric — an ecosystem of devices, edge and cloud computation based on latency, energy and priority constraints. This tight coupling of connectivity and computing will enable real-time inference, adaptive workload placement and collective machine intelligence.

New technologies will allow us to interact with devices directly and more efficiently. Combined with digital twins and simulation environments, these closed loops extend autonomy from machines to human-machine co-agency.

Convergence will become real where:

In this vision, autonomy is not localized but distributed across a cooperative ecosystem of devices, infrastructure and human users. Intelligence emerges from the system, not the node.

A smart city doesn't just transmit data – it perceives, reasons, and acts. A smart vehicle doesn't just drive – it collaborates with infrastructure, pedestrians and fleets. A smart healthcare system doesn't just monitor – it anticipates, adapts and interfaces with cognition itself.

Autonomy is not a module. It is the emergent property of a secure, converged, context-aware system. By tightly weaving sensing, computing, control and connectivity, we can build systems that are resilient, adaptive, and purpose-driven.

The future of autonomy lies not in replacing humans but in augmenting and partnering with them – through systems that learn, reason and act with awareness. This is the architecture of an intelligent society and how we build truly autonomous systems.