Introduction
This article analyzes the transition from static AI models to embodied systems. It argues that intelligence is not merely a result of computation, but rather emerges from the body's interaction with the physical world.
The reader will discover why embodied cognition and biological emergence are critical for building autonomous agents. The text explains the difference between symbol processing and active participation in physical reality.
The Body as an Active Cognitive Mechanism
Embodiment is the thesis that the structure of the body co-determines thought processes. The body is not a peripheral device providing data, but an integral part of the cognitive system.
Physical structure determines so-called affordances, or the possibilities for action within an environment. For example, a step has a different meaning for a human than it does for a wheeled robot.
Consequently, intelligence becomes the result of a feedback loop between morphology and perception. This impacts control systems, as the system does not compute everything centrally but utilizes morphological computation, where the physical material handles part of the processing.
World Resistance as a Verifier of Erroneous Hypotheses
The knowledge held by language models is a statistical trace of human experiences. An embodied agent, however, possesses indexical knowledge resulting from direct contact with matter.
The key is the resistance of the world. While an error in an LLM is merely an incorrect sentence, a physical agent's error results in dropping an object or a collision.
Such a correction mechanism forces the development of true autonomy. Action becomes a cognitive experiment that reduces uncertainty in a way that is inaccessible to disembodied systems.
Autonomy as the Ability to Maintain One's Own Organization
The intelligence of a language model differs from biological autonomy due to a lack of autopoiesis. A living organism independently regenerates the conditions of its own existence, whereas AI depends on external infrastructure.
True autonomy requires a closed loop: the state of the body influences action, and the world's reaction modifies the model. This allows for the creation of an individual trajectory of experience and a functional biography.
In biology, this is evident in morphogenesis. Cells build tissues without a central blueprint, utilizing bioelectricity and self-organization. An example is Anthrobots, which prove that organization does not result solely from DNA encoding.
Summary
Embodiment shifts the definition of intelligence from the realm of world representation toward active participation in changing that world. This does not require anthropomorphism, but rather the creation of systems capable of learning through physical resistance.
We are entering an era of technologies based on controlling trajectories of complexity that we can no longer fully manage with traditional logic. The greatest risk may be our helplessness in the face of the effectiveness of solutions whose mechanisms we do not know how to understand.
Frequently Asked Questions
What is embodiment in the context of intelligence, and why is the body not merely a periphery for the mind?
Embodiment is a concept according to which intelligence is the result of the dynamic interaction between the brain, the body, and the environment. The body is not merely a periphery because its structure and morphology co-determine available information and shape the way the world is perceived through relations of action possibilities (affordances).
How does a language model's knowledge of the world differ from that of a physically embodied agent?
A language model's knowledge is based on the statistical structure of texts that are traces of human embodiment, whereas a physical agent acquires knowledge through direct sensory-motor experience. For a physical agent, an error in assessing the world becomes an event with concrete consequences (e.g., dropping an object), which constitutes an independent system for selecting erroneous hypotheses that text models lack.
How does the intelligence of a language model differ from the autonomy of a living organism in the context of interacting with the world?
The autonomy of a living organism consists of the ability to self-produce and maintain the conditions of its own existence, whereas a language model is dependent on external infrastructure. Unlike organisms, LLM models typically do not regenerate the conditions of their being nor perform autonomous self-maintenance.
How do physical body structure and environment influence intelligence and control processes?
The physical structure of the body allows for so-called morphological computation, where appropriately shaped matter takes over part of the control problems, simplifying the work of the nervous system. Simultaneously, the environment serves as an external information resource and cognitive scaffolding, allowing the system to distribute intelligence processes between the controller, morphology, and environment.
Does artificial intelligence have to be conscious to realistically change the way we think and process information?
No, artificial intelligence does not need to be conscious to realistically change the way of thinking and processing information. It can function as an element of a human's extended cognitive system, influencing memory or the formulation of arguments without possessing its own mind.
How do the physical body and environmental constraints change the way an agent acquires knowledge compared to disembodied models?
Unlike disembodied models, an embodied agent ceases to be a passive recipient of data and can actively acquire knowledge through action and experimentation. Physical constraints, such as locality, finite sensor range, or the irreversibility of events, force it to prioritize attention and develop complex decision-making strategies under conditions of partial observability.
To create truly intelligent AI, must we build a human-like robot?
No, building a human-like robot is not a prerequisite for creating intelligent AI. General intelligence can develop through multiple or reconfigurable embodiment, where the system controls various morphologies and utilizes sensors unavailable to humans.
How does embodiment change the learning process of artificial intelligence, and what does this mean for the concept of agency and the future of work?
Embodiment ensures that AI stops relying solely on historical data and begins generating its own data through action, creating an individual trajectory of experience and a functional equivalent of a biography. Agency in such a system could result from a process of gradual individualization arising from following a specific path within a physical body. For the future of work, this means a breakthrough where AI enters the domain of physical practices (e.g., care, construction, or logistics), rather than just automating cognitive tasks.
Why is simply having a body not enough to create intelligence, and what is the role of the world's physical resistance in this process?
Simply having a body is not enough because intelligence requires interaction with an unpredictable environment that acts as a model correction mechanism. The physical resistance of the world sets limits on what can be simulated, forcing the agent to update its model in response to errors, such as a misunderstanding of friction or trajectory.
How does embodied AI differ from traditional models, and how does biology explain the emergence of complex structures without central control?
Embodied AI differs from traditional models in that it stops merely representing the world and becomes a participant in its changes, using the body and environment to actively acquire data. In biology, the emergence of complex structures without central control is explained by emergence, where global form results from the coordinated dynamics of local cellular processes.
How do organisms build and regenerate their bodies without a central architectural plan or conscious intent?
Organisms build and regenerate their bodies through teleonomy—processes guided by natural selection and regulatory physiology. The final anatomy emerges from dynamics including gene expression, intercellular signaling, bioelectricity, and tissue mechanics, which together lead the system toward stable phenotypic states.
What is morphological memory and how do Anthrobots prove that biological organization does not result solely from the record in DNA?
Morphological memory is the maintenance of information within the dynamic states of regulatory networks, allowing tissue to strive toward a specific anatomy without needing an image of its previous form. Anthrobots prove that biological organization does not result solely from DNA because these cells can organize themselves into a new anatomy and exhibit new behaviors depending on conditions and intercellular interactions.
Do cell self-organization and emergent system behaviors mean that they possess consciousness or intelligence in the human sense?
No, self-organization and emergent behaviors are not evidence of cellular intelligence in the common scientific sense nor the presence of consciousness. A system's drive toward a specific state is merely a systemic mechanism, and treating it as a manifestation of a conscious subject would be incorrect.
Do AI successes in predicting protein structures prove that biology is essentially an information system?
No, AlphaFold's successes do not prove that the materiality of biology can be reduced to an information system. They only show the existence of regularities between amino acid sequences and protein structures, while the actual functioning of organisms depends on complex physical and chemical processes.
How does the combination of AI and biology change the approach to medicine and the engineering of complex systems?
The combination of AI and biology transforms medicine by shifting from repairing single molecules to controlling tissue self-organization processes and modeling protein structures. This changes the approach to complex systems engineering, where instead of rigid programming, conditions and signals are designed to guide the system toward desired effects.
Do intelligence and agency appear suddenly at a certain point, or are they the result of gradual systemic organization?
Intelligence does not appear at one magic point but is the result of the gradual organization of relations and a ladder of organizational capabilities. Competencies emerge through the integration of elements into a whole, allowing for the creation of functions unavailable to individual components.
What are the practical consequences of understanding emergence for the future of AI and medicine?
Understanding emergence leads to the creation of a new field of complexity design, where instead of building rigid mechanisms, one learns to control systems with their own dynamics and regenerative capabilities. In medicine, this means a transition from repairing individual elements to managing the trajectory of the entire organism, utilizing AI and synthetic biology to consciously shape the human body.
